{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P</th>\n",
       "      <th>Ph-24</th>\n",
       "      <th>Ph-25</th>\n",
       "      <th>Ph-26</th>\n",
       "      <th>Ph-27</th>\n",
       "      <th>Ph-28</th>\n",
       "      <th>Ph-29</th>\n",
       "      <th>Ph-30</th>\n",
       "      <th>Ph-31</th>\n",
       "      <th>Ph-32</th>\n",
       "      <th>...</th>\n",
       "      <th>month_Dec</th>\n",
       "      <th>month_Feb</th>\n",
       "      <th>month_Jan</th>\n",
       "      <th>month_Jul</th>\n",
       "      <th>month_Jun</th>\n",
       "      <th>month_Mar</th>\n",
       "      <th>month_May</th>\n",
       "      <th>month_Nov</th>\n",
       "      <th>month_Oct</th>\n",
       "      <th>month_Sep</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fecha_hora</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-08-09 00:00:00</th>\n",
       "      <td>95.3264</td>\n",
       "      <td>54.8461</td>\n",
       "      <td>45.1982</td>\n",
       "      <td>44.7123</td>\n",
       "      <td>46.2995</td>\n",
       "      <td>42.9422</td>\n",
       "      <td>44.3551</td>\n",
       "      <td>46.3440</td>\n",
       "      <td>45.6826</td>\n",
       "      <td>42.6676</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-09 01:00:00</th>\n",
       "      <td>82.8532</td>\n",
       "      <td>52.2179</td>\n",
       "      <td>54.8461</td>\n",
       "      <td>45.1982</td>\n",
       "      <td>44.7123</td>\n",
       "      <td>46.2995</td>\n",
       "      <td>42.9422</td>\n",
       "      <td>44.3551</td>\n",
       "      <td>46.3440</td>\n",
       "      <td>45.6826</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-09 02:00:00</th>\n",
       "      <td>57.9764</td>\n",
       "      <td>61.9198</td>\n",
       "      <td>52.2179</td>\n",
       "      <td>54.8461</td>\n",
       "      <td>45.1982</td>\n",
       "      <td>44.7123</td>\n",
       "      <td>46.2995</td>\n",
       "      <td>42.9422</td>\n",
       "      <td>44.3551</td>\n",
       "      <td>46.3440</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-09 03:00:00</th>\n",
       "      <td>67.6407</td>\n",
       "      <td>36.8105</td>\n",
       "      <td>61.9198</td>\n",
       "      <td>52.2179</td>\n",
       "      <td>54.8461</td>\n",
       "      <td>45.1982</td>\n",
       "      <td>44.7123</td>\n",
       "      <td>46.2995</td>\n",
       "      <td>42.9422</td>\n",
       "      <td>44.3551</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-09 04:00:00</th>\n",
       "      <td>66.4892</td>\n",
       "      <td>34.8274</td>\n",
       "      <td>36.8105</td>\n",
       "      <td>61.9198</td>\n",
       "      <td>52.2179</td>\n",
       "      <td>54.8461</td>\n",
       "      <td>45.1982</td>\n",
       "      <td>44.7123</td>\n",
       "      <td>46.2995</td>\n",
       "      <td>42.9422</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           P    Ph-24    Ph-25    Ph-26    Ph-27    Ph-28  \\\n",
       "Fecha_hora                                                                  \n",
       "2016-08-09 00:00:00  95.3264  54.8461  45.1982  44.7123  46.2995  42.9422   \n",
       "2016-08-09 01:00:00  82.8532  52.2179  54.8461  45.1982  44.7123  46.2995   \n",
       "2016-08-09 02:00:00  57.9764  61.9198  52.2179  54.8461  45.1982  44.7123   \n",
       "2016-08-09 03:00:00  67.6407  36.8105  61.9198  52.2179  54.8461  45.1982   \n",
       "2016-08-09 04:00:00  66.4892  34.8274  36.8105  61.9198  52.2179  54.8461   \n",
       "\n",
       "                       Ph-29    Ph-30    Ph-31    Ph-32    ...      month_Dec  \\\n",
       "Fecha_hora                                                 ...                  \n",
       "2016-08-09 00:00:00  44.3551  46.3440  45.6826  42.6676    ...              0   \n",
       "2016-08-09 01:00:00  42.9422  44.3551  46.3440  45.6826    ...              0   \n",
       "2016-08-09 02:00:00  46.2995  42.9422  44.3551  46.3440    ...              0   \n",
       "2016-08-09 03:00:00  44.7123  46.2995  42.9422  44.3551    ...              0   \n",
       "2016-08-09 04:00:00  45.1982  44.7123  46.2995  42.9422    ...              0   \n",
       "\n",
       "                     month_Feb  month_Jan  month_Jul  month_Jun  month_Mar  \\\n",
       "Fecha_hora                                                                   \n",
       "2016-08-09 00:00:00          0          0          0          0          0   \n",
       "2016-08-09 01:00:00          0          0          0          0          0   \n",
       "2016-08-09 02:00:00          0          0          0          0          0   \n",
       "2016-08-09 03:00:00          0          0          0          0          0   \n",
       "2016-08-09 04:00:00          0          0          0          0          0   \n",
       "\n",
       "                     month_May  month_Nov  month_Oct  month_Sep  \n",
       "Fecha_hora                                                       \n",
       "2016-08-09 00:00:00          0          0          0          0  \n",
       "2016-08-09 01:00:00          0          0          0          0  \n",
       "2016-08-09 02:00:00          0          0          0          0  \n",
       "2016-08-09 03:00:00          0          0          0          0  \n",
       "2016-08-09 04:00:00          0          0          0          0  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fname = \"lbrprices_transformed_v2.csv\"\n",
    "prices = pd.read_csv(fname, index_col=\"Fecha_hora\").dropna()\n",
    "prices.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P</th>\n",
       "      <th>Ph-24</th>\n",
       "      <th>Ph-25</th>\n",
       "      <th>Ph-26</th>\n",
       "      <th>Ph-27</th>\n",
       "      <th>Ph-28</th>\n",
       "      <th>Ph-29</th>\n",
       "      <th>Ph-30</th>\n",
       "      <th>Ph-31</th>\n",
       "      <th>Ph-32</th>\n",
       "      <th>...</th>\n",
       "      <th>month_Dec</th>\n",
       "      <th>month_Feb</th>\n",
       "      <th>month_Jan</th>\n",
       "      <th>month_Jul</th>\n",
       "      <th>month_Jun</th>\n",
       "      <th>month_Mar</th>\n",
       "      <th>month_May</th>\n",
       "      <th>month_Nov</th>\n",
       "      <th>month_Oct</th>\n",
       "      <th>month_Sep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20520.000000</td>\n",
       "      <td>20520.000000</td>\n",
       "      <td>20520.000000</td>\n",
       "      <td>20520.000000</td>\n",
       "      <td>20520.000000</td>\n",
       "      <td>20520.000000</td>\n",
       "      <td>20520.000000</td>\n",
       "      <td>20520.000000</td>\n",
       "      <td>20520.000000</td>\n",
       "      <td>20520.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>20520.000000</td>\n",
       "      <td>20520.000000</td>\n",
       "      <td>20520.000000</td>\n",
       "      <td>20520.000000</td>\n",
       "      <td>20520.000000</td>\n",
       "      <td>20520.000000</td>\n",
       "      <td>20520.000000</td>\n",
       "      <td>20520.000000</td>\n",
       "      <td>20520.000000</td>\n",
       "      <td>20520.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>72.093873</td>\n",
       "      <td>72.053371</td>\n",
       "      <td>72.051262</td>\n",
       "      <td>72.048388</td>\n",
       "      <td>72.043583</td>\n",
       "      <td>72.039969</td>\n",
       "      <td>72.035831</td>\n",
       "      <td>72.032904</td>\n",
       "      <td>72.029738</td>\n",
       "      <td>72.025476</td>\n",
       "      <td>...</td>\n",
       "      <td>0.085380</td>\n",
       "      <td>0.065497</td>\n",
       "      <td>0.072515</td>\n",
       "      <td>0.072515</td>\n",
       "      <td>0.070175</td>\n",
       "      <td>0.072515</td>\n",
       "      <td>0.072515</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.108772</td>\n",
       "      <td>0.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>34.183615</td>\n",
       "      <td>34.176714</td>\n",
       "      <td>34.177036</td>\n",
       "      <td>34.176854</td>\n",
       "      <td>34.173544</td>\n",
       "      <td>34.172700</td>\n",
       "      <td>34.170911</td>\n",
       "      <td>34.170539</td>\n",
       "      <td>34.169971</td>\n",
       "      <td>34.168179</td>\n",
       "      <td>...</td>\n",
       "      <td>0.279453</td>\n",
       "      <td>0.247407</td>\n",
       "      <td>0.259344</td>\n",
       "      <td>0.259344</td>\n",
       "      <td>0.255449</td>\n",
       "      <td>0.259344</td>\n",
       "      <td>0.259344</td>\n",
       "      <td>0.306900</td>\n",
       "      <td>0.311360</td>\n",
       "      <td>0.306900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.704900</td>\n",
       "      <td>18.704900</td>\n",
       "      <td>18.704900</td>\n",
       "      <td>18.704900</td>\n",
       "      <td>18.704900</td>\n",
       "      <td>18.704900</td>\n",
       "      <td>18.704900</td>\n",
       "      <td>18.704900</td>\n",
       "      <td>18.704900</td>\n",
       "      <td>18.704900</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48.164400</td>\n",
       "      <td>48.156475</td>\n",
       "      <td>48.154175</td>\n",
       "      <td>48.152250</td>\n",
       "      <td>48.147300</td>\n",
       "      <td>48.145375</td>\n",
       "      <td>48.140500</td>\n",
       "      <td>48.134600</td>\n",
       "      <td>48.132500</td>\n",
       "      <td>48.130500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>65.084300</td>\n",
       "      <td>65.010100</td>\n",
       "      <td>65.008500</td>\n",
       "      <td>65.007250</td>\n",
       "      <td>65.002250</td>\n",
       "      <td>64.996600</td>\n",
       "      <td>64.995100</td>\n",
       "      <td>64.993600</td>\n",
       "      <td>64.989550</td>\n",
       "      <td>64.985950</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>88.620925</td>\n",
       "      <td>88.541800</td>\n",
       "      <td>88.541800</td>\n",
       "      <td>88.531450</td>\n",
       "      <td>88.526450</td>\n",
       "      <td>88.521425</td>\n",
       "      <td>88.517825</td>\n",
       "      <td>88.516725</td>\n",
       "      <td>88.513675</td>\n",
       "      <td>88.496425</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>329.440000</td>\n",
       "      <td>329.440000</td>\n",
       "      <td>329.440000</td>\n",
       "      <td>329.440000</td>\n",
       "      <td>329.440000</td>\n",
       "      <td>329.440000</td>\n",
       "      <td>329.440000</td>\n",
       "      <td>329.440000</td>\n",
       "      <td>329.440000</td>\n",
       "      <td>329.440000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  P         Ph-24         Ph-25         Ph-26         Ph-27  \\\n",
       "count  20520.000000  20520.000000  20520.000000  20520.000000  20520.000000   \n",
       "mean      72.093873     72.053371     72.051262     72.048388     72.043583   \n",
       "std       34.183615     34.176714     34.177036     34.176854     34.173544   \n",
       "min       18.704900     18.704900     18.704900     18.704900     18.704900   \n",
       "25%       48.164400     48.156475     48.154175     48.152250     48.147300   \n",
       "50%       65.084300     65.010100     65.008500     65.007250     65.002250   \n",
       "75%       88.620925     88.541800     88.541800     88.531450     88.526450   \n",
       "max      329.440000    329.440000    329.440000    329.440000    329.440000   \n",
       "\n",
       "              Ph-28         Ph-29         Ph-30         Ph-31         Ph-32  \\\n",
       "count  20520.000000  20520.000000  20520.000000  20520.000000  20520.000000   \n",
       "mean      72.039969     72.035831     72.032904     72.029738     72.025476   \n",
       "std       34.172700     34.170911     34.170539     34.169971     34.168179   \n",
       "min       18.704900     18.704900     18.704900     18.704900     18.704900   \n",
       "25%       48.145375     48.140500     48.134600     48.132500     48.130500   \n",
       "50%       64.996600     64.995100     64.993600     64.989550     64.985950   \n",
       "75%       88.521425     88.517825     88.516725     88.513675     88.496425   \n",
       "max      329.440000    329.440000    329.440000    329.440000    329.440000   \n",
       "\n",
       "           ...          month_Dec     month_Feb     month_Jan     month_Jul  \\\n",
       "count      ...       20520.000000  20520.000000  20520.000000  20520.000000   \n",
       "mean       ...           0.085380      0.065497      0.072515      0.072515   \n",
       "std        ...           0.279453      0.247407      0.259344      0.259344   \n",
       "min        ...           0.000000      0.000000      0.000000      0.000000   \n",
       "25%        ...           0.000000      0.000000      0.000000      0.000000   \n",
       "50%        ...           0.000000      0.000000      0.000000      0.000000   \n",
       "75%        ...           0.000000      0.000000      0.000000      0.000000   \n",
       "max        ...           1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "          month_Jun     month_Mar     month_May     month_Nov     month_Oct  \\\n",
       "count  20520.000000  20520.000000  20520.000000  20520.000000  20520.000000   \n",
       "mean       0.070175      0.072515      0.072515      0.105263      0.108772   \n",
       "std        0.255449      0.259344      0.259344      0.306900      0.311360   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "          month_Sep  \n",
       "count  20520.000000  \n",
       "mean       0.105263  \n",
       "std        0.306900  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max        1.000000  \n",
       "\n",
       "[8 rows x 94 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.describe(include=['object', 'int', 'float'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choosing features and targe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = prices.drop([\"P\"], axis=1)\n",
    "y = prices[\"P\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fecha_hora\n",
       "2016-08-09 00:00:00    95.3264\n",
       "2016-08-09 01:00:00    82.8532\n",
       "2016-08-09 02:00:00    57.9764\n",
       "2016-08-09 03:00:00    67.6407\n",
       "2016-08-09 04:00:00    66.4892\n",
       "Name: P, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ph-24</th>\n",
       "      <th>Ph-25</th>\n",
       "      <th>Ph-26</th>\n",
       "      <th>Ph-27</th>\n",
       "      <th>Ph-28</th>\n",
       "      <th>Ph-29</th>\n",
       "      <th>Ph-30</th>\n",
       "      <th>Ph-31</th>\n",
       "      <th>Ph-32</th>\n",
       "      <th>Ph-33</th>\n",
       "      <th>...</th>\n",
       "      <th>month_Dec</th>\n",
       "      <th>month_Feb</th>\n",
       "      <th>month_Jan</th>\n",
       "      <th>month_Jul</th>\n",
       "      <th>month_Jun</th>\n",
       "      <th>month_Mar</th>\n",
       "      <th>month_May</th>\n",
       "      <th>month_Nov</th>\n",
       "      <th>month_Oct</th>\n",
       "      <th>month_Sep</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fecha_hora</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-08-09 00:00:00</th>\n",
       "      <td>54.8461</td>\n",
       "      <td>45.1982</td>\n",
       "      <td>44.7123</td>\n",
       "      <td>46.2995</td>\n",
       "      <td>42.9422</td>\n",
       "      <td>44.3551</td>\n",
       "      <td>46.3440</td>\n",
       "      <td>45.6826</td>\n",
       "      <td>42.6676</td>\n",
       "      <td>39.0872</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-09 01:00:00</th>\n",
       "      <td>52.2179</td>\n",
       "      <td>54.8461</td>\n",
       "      <td>45.1982</td>\n",
       "      <td>44.7123</td>\n",
       "      <td>46.2995</td>\n",
       "      <td>42.9422</td>\n",
       "      <td>44.3551</td>\n",
       "      <td>46.3440</td>\n",
       "      <td>45.6826</td>\n",
       "      <td>42.6676</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-09 02:00:00</th>\n",
       "      <td>61.9198</td>\n",
       "      <td>52.2179</td>\n",
       "      <td>54.8461</td>\n",
       "      <td>45.1982</td>\n",
       "      <td>44.7123</td>\n",
       "      <td>46.2995</td>\n",
       "      <td>42.9422</td>\n",
       "      <td>44.3551</td>\n",
       "      <td>46.3440</td>\n",
       "      <td>45.6826</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-09 03:00:00</th>\n",
       "      <td>36.8105</td>\n",
       "      <td>61.9198</td>\n",
       "      <td>52.2179</td>\n",
       "      <td>54.8461</td>\n",
       "      <td>45.1982</td>\n",
       "      <td>44.7123</td>\n",
       "      <td>46.2995</td>\n",
       "      <td>42.9422</td>\n",
       "      <td>44.3551</td>\n",
       "      <td>46.3440</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-09 04:00:00</th>\n",
       "      <td>34.8274</td>\n",
       "      <td>36.8105</td>\n",
       "      <td>61.9198</td>\n",
       "      <td>52.2179</td>\n",
       "      <td>54.8461</td>\n",
       "      <td>45.1982</td>\n",
       "      <td>44.7123</td>\n",
       "      <td>46.2995</td>\n",
       "      <td>42.9422</td>\n",
       "      <td>44.3551</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 93 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Ph-24    Ph-25    Ph-26    Ph-27    Ph-28    Ph-29  \\\n",
       "Fecha_hora                                                                  \n",
       "2016-08-09 00:00:00  54.8461  45.1982  44.7123  46.2995  42.9422  44.3551   \n",
       "2016-08-09 01:00:00  52.2179  54.8461  45.1982  44.7123  46.2995  42.9422   \n",
       "2016-08-09 02:00:00  61.9198  52.2179  54.8461  45.1982  44.7123  46.2995   \n",
       "2016-08-09 03:00:00  36.8105  61.9198  52.2179  54.8461  45.1982  44.7123   \n",
       "2016-08-09 04:00:00  34.8274  36.8105  61.9198  52.2179  54.8461  45.1982   \n",
       "\n",
       "                       Ph-30    Ph-31    Ph-32    Ph-33    ...      month_Dec  \\\n",
       "Fecha_hora                                                 ...                  \n",
       "2016-08-09 00:00:00  46.3440  45.6826  42.6676  39.0872    ...              0   \n",
       "2016-08-09 01:00:00  44.3551  46.3440  45.6826  42.6676    ...              0   \n",
       "2016-08-09 02:00:00  42.9422  44.3551  46.3440  45.6826    ...              0   \n",
       "2016-08-09 03:00:00  46.2995  42.9422  44.3551  46.3440    ...              0   \n",
       "2016-08-09 04:00:00  44.7123  46.2995  42.9422  44.3551    ...              0   \n",
       "\n",
       "                     month_Feb  month_Jan  month_Jul  month_Jun  month_Mar  \\\n",
       "Fecha_hora                                                                   \n",
       "2016-08-09 00:00:00          0          0          0          0          0   \n",
       "2016-08-09 01:00:00          0          0          0          0          0   \n",
       "2016-08-09 02:00:00          0          0          0          0          0   \n",
       "2016-08-09 03:00:00          0          0          0          0          0   \n",
       "2016-08-09 04:00:00          0          0          0          0          0   \n",
       "\n",
       "                     month_May  month_Nov  month_Oct  month_Sep  \n",
       "Fecha_hora                                                       \n",
       "2016-08-09 00:00:00          0          0          0          0  \n",
       "2016-08-09 01:00:00          0          0          0          0  \n",
       "2016-08-09 02:00:00          0          0          0          0  \n",
       "2016-08-09 03:00:00          0          0          0          0  \n",
       "2016-08-09 04:00:00          0          0          0          0  \n",
       "\n",
       "[5 rows x 93 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train/test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training features/target:  (15390, 93) (15390,)\n",
      "Testing freatures/targe:  (5130, 93) (5130,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, random_state = 1)\n",
    "print(\"Training features/target: \", X_train.shape, y_train.shape)\n",
    "print(\"Testing freatures/targe: \", X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scalaling Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_train = y_train.values.reshape(-1 ,1)\n",
    "y_test = y_test.values.reshape(-1 , 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Cross-Validation and Hyper-parameters | Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import Pipeline and GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pipe = Pipeline([\n",
    "    ('standarscaler', StandardScaler()),\n",
    "    ('regressor', DecisionTreeRegressor(max_depth=8))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 13 candidates, totalling 65 fits\n",
      "[CV] regressor__max_depth=3 ..........................................\n",
      "[CV] . regressor__max_depth=3, score=0.5169398553533737, total=   0.4s\n",
      "[CV] regressor__max_depth=3 ..........................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.4s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] . regressor__max_depth=3, score=0.5292431314738588, total=   0.4s\n",
      "[CV] regressor__max_depth=3 ..........................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.8s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] . regressor__max_depth=3, score=0.5271596622666745, total=   0.4s\n",
      "[CV] regressor__max_depth=3 ..........................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    1.3s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] . regressor__max_depth=3, score=0.5189516721105789, total=   0.4s\n",
      "[CV] regressor__max_depth=3 ..........................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    1.7s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] . regressor__max_depth=3, score=0.5328275859007272, total=   0.4s\n",
      "[CV] regressor__max_depth=4 ..........................................\n",
      "[CV] . regressor__max_depth=4, score=0.5569892983602978, total=   0.5s\n",
      "[CV] regressor__max_depth=4 ..........................................\n",
      "[CV] . regressor__max_depth=4, score=0.5718718945800592, total=   0.5s\n",
      "[CV] regressor__max_depth=4 ..........................................\n",
      "[CV] . regressor__max_depth=4, score=0.5705645799913583, total=   0.5s\n",
      "[CV] regressor__max_depth=4 ..........................................\n",
      "[CV] . regressor__max_depth=4, score=0.5683469295684258, total=   0.5s\n",
      "[CV] regressor__max_depth=4 ..........................................\n",
      "[CV] . regressor__max_depth=4, score=0.5634864084931571, total=   0.5s\n",
      "[CV] regressor__max_depth=5 ..........................................\n",
      "[CV] . regressor__max_depth=5, score=0.5755784603496508, total=   0.6s\n",
      "[CV] regressor__max_depth=5 ..........................................\n",
      "[CV] . regressor__max_depth=5, score=0.6070172485297564, total=   0.6s\n",
      "[CV] regressor__max_depth=5 ..........................................\n",
      "[CV] . regressor__max_depth=5, score=0.5976264926331821, total=   0.7s\n",
      "[CV] regressor__max_depth=5 ..........................................\n",
      "[CV] . regressor__max_depth=5, score=0.5802800801870673, total=   0.6s\n",
      "[CV] regressor__max_depth=5 ..........................................\n",
      "[CV] . regressor__max_depth=5, score=0.5909206393516135, total=   0.6s\n",
      "[CV] regressor__max_depth=6 ..........................................\n",
      "[CV] . regressor__max_depth=6, score=0.6059512783528294, total=   0.7s\n",
      "[CV] regressor__max_depth=6 ..........................................\n",
      "[CV] .. regressor__max_depth=6, score=0.628302461531919, total=   0.7s\n",
      "[CV] regressor__max_depth=6 ..........................................\n",
      "[CV] . regressor__max_depth=6, score=0.5939095662582892, total=   0.7s\n",
      "[CV] regressor__max_depth=6 ..........................................\n",
      "[CV] .. regressor__max_depth=6, score=0.602608087879215, total=   0.7s\n",
      "[CV] regressor__max_depth=6 ..........................................\n",
      "[CV] . regressor__max_depth=6, score=0.6212872388320132, total=   0.7s\n",
      "[CV] regressor__max_depth=7 ..........................................\n",
      "[CV] . regressor__max_depth=7, score=0.6137124228075161, total=   0.9s\n",
      "[CV] regressor__max_depth=7 ..........................................\n",
      "[CV] . regressor__max_depth=7, score=0.6151119363908413, total=   0.9s\n",
      "[CV] regressor__max_depth=7 ..........................................\n",
      "[CV] .... regressor__max_depth=7, score=0.6035273269369, total=   0.9s\n",
      "[CV] regressor__max_depth=7 ..........................................\n",
      "[CV] . regressor__max_depth=7, score=0.6169263342556857, total=   0.9s\n",
      "[CV] regressor__max_depth=7 ..........................................\n",
      "[CV] . regressor__max_depth=7, score=0.6312721893001614, total=   0.9s\n",
      "[CV] regressor__max_depth=8 ..........................................\n",
      "[CV] .. regressor__max_depth=8, score=0.616090697304017, total=   1.1s\n",
      "[CV] regressor__max_depth=8 ..........................................\n",
      "[CV] . regressor__max_depth=8, score=0.6085141359390052, total=   1.0s\n",
      "[CV] regressor__max_depth=8 ..........................................\n",
      "[CV] . regressor__max_depth=8, score=0.5804836936155511, total=   0.7s\n",
      "[CV] regressor__max_depth=8 ..........................................\n",
      "[CV] . regressor__max_depth=8, score=0.6298486549529034, total=   0.7s\n",
      "[CV] regressor__max_depth=8 ..........................................\n",
      "[CV] . regressor__max_depth=8, score=0.6288574935330786, total=   0.7s\n",
      "[CV] regressor__max_depth=9 ..........................................\n",
      "[CV] . regressor__max_depth=9, score=0.6070635264279656, total=   0.8s\n",
      "[CV] regressor__max_depth=9 ..........................................\n",
      "[CV] . regressor__max_depth=9, score=0.6079616981779048, total=   0.9s\n",
      "[CV] regressor__max_depth=9 ..........................................\n",
      "[CV] . regressor__max_depth=9, score=0.6060849679553317, total=   0.8s\n",
      "[CV] regressor__max_depth=9 ..........................................\n",
      "[CV] . regressor__max_depth=9, score=0.6342373366266129, total=   0.8s\n",
      "[CV] regressor__max_depth=9 ..........................................\n",
      "[CV] . regressor__max_depth=9, score=0.6195804618417622, total=   1.0s\n",
      "[CV] regressor__max_depth=10 .........................................\n",
      "[CV]  regressor__max_depth=10, score=0.5919827391435091, total=   1.0s\n",
      "[CV] regressor__max_depth=10 .........................................\n",
      "[CV] . regressor__max_depth=10, score=0.591163529137293, total=   0.9s\n",
      "[CV] regressor__max_depth=10 .........................................\n",
      "[CV]  regressor__max_depth=10, score=0.6058360615686575, total=   0.9s\n",
      "[CV] regressor__max_depth=10 .........................................\n",
      "[CV]  regressor__max_depth=10, score=0.6424703429332514, total=   0.9s\n",
      "[CV] regressor__max_depth=10 .........................................\n",
      "[CV]  regressor__max_depth=10, score=0.6398478330529145, total=   0.9s\n",
      "[CV] regressor__max_depth=11 .........................................\n",
      "[CV]  regressor__max_depth=11, score=0.5701732792622907, total=   1.0s\n",
      "[CV] regressor__max_depth=11 .........................................\n",
      "[CV]  regressor__max_depth=11, score=0.5661969680976917, total=   1.3s\n",
      "[CV] regressor__max_depth=11 .........................................\n",
      "[CV]  regressor__max_depth=11, score=0.5844198200947085, total=   1.1s\n",
      "[CV] regressor__max_depth=11 .........................................\n",
      "[CV]  regressor__max_depth=11, score=0.6473912426875859, total=   0.9s\n",
      "[CV] regressor__max_depth=11 .........................................\n",
      "[CV]  regressor__max_depth=11, score=0.6478204733367422, total=   1.0s\n",
      "[CV] regressor__max_depth=12 .........................................\n",
      "[CV] . regressor__max_depth=12, score=0.564342995041403, total=   1.0s\n",
      "[CV] regressor__max_depth=12 .........................................\n",
      "[CV] .. regressor__max_depth=12, score=0.56464709815865, total=   1.0s\n",
      "[CV] regressor__max_depth=12 .........................................\n",
      "[CV]  regressor__max_depth=12, score=0.5691740864798083, total=   1.1s\n",
      "[CV] regressor__max_depth=12 .........................................\n",
      "[CV]  regressor__max_depth=12, score=0.6392364315887575, total=   1.1s\n",
      "[CV] regressor__max_depth=12 .........................................\n",
      "[CV]  regressor__max_depth=12, score=0.6384692454062504, total=   1.0s\n",
      "[CV] regressor__max_depth=13 .........................................\n",
      "[CV]  regressor__max_depth=13, score=0.5523438620310935, total=   1.1s\n",
      "[CV] regressor__max_depth=13 .........................................\n",
      "[CV]  regressor__max_depth=13, score=0.5430819073602634, total=   1.1s\n",
      "[CV] regressor__max_depth=13 .........................................\n",
      "[CV]  regressor__max_depth=13, score=0.5327778363292415, total=   1.1s\n",
      "[CV] regressor__max_depth=13 .........................................\n",
      "[CV]  regressor__max_depth=13, score=0.6079231207296709, total=   1.1s\n",
      "[CV] regressor__max_depth=13 .........................................\n",
      "[CV]  regressor__max_depth=13, score=0.6267399814895201, total=   1.1s\n",
      "[CV] regressor__max_depth=14 .........................................\n",
      "[CV]  regressor__max_depth=14, score=0.5375847241312031, total=   1.1s\n",
      "[CV] regressor__max_depth=14 .........................................\n",
      "[CV]  regressor__max_depth=14, score=0.5090921616961444, total=   1.2s\n",
      "[CV] regressor__max_depth=14 .........................................\n",
      "[CV]  regressor__max_depth=14, score=0.5328989388967353, total=   2.0s\n",
      "[CV] regressor__max_depth=14 .........................................\n",
      "[CV]  regressor__max_depth=14, score=0.6161752692167073, total=   2.3s\n",
      "[CV] regressor__max_depth=14 .........................................\n",
      "[CV] . regressor__max_depth=14, score=0.602342823407813, total=   1.7s\n",
      "[CV] regressor__max_depth=15 .........................................\n",
      "[CV]  regressor__max_depth=15, score=0.5270248181929342, total=   1.7s\n",
      "[CV] regressor__max_depth=15 .........................................\n",
      "[CV]  regressor__max_depth=15, score=0.5106190857782906, total=   1.3s\n",
      "[CV] regressor__max_depth=15 .........................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  regressor__max_depth=15, score=0.5544623006601073, total=   1.2s\n",
      "[CV] regressor__max_depth=15 .........................................\n",
      "[CV]  regressor__max_depth=15, score=0.5787527037563012, total=   1.2s\n",
      "[CV] regressor__max_depth=15 .........................................\n",
      "[CV] . regressor__max_depth=15, score=0.591278105782729, total=   1.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  65 out of  65 | elapsed:  1.0min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.65082387928641783"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hyperparameters = {'regressor__max_depth': range(3,16)}\n",
    "\n",
    "rgs = GridSearchCV(pipe, hyperparameters, cv=5, scoring='r2', verbose=5)\n",
    "rgs.fit(X_train, y_train)\n",
    "rgs.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.65082387928641783"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rgs.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'regressor__max_depth': 7}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rgs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = rgs.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"989\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_test[2000:2100], 'o')\n",
    "plt.plot(y_pred[2000:2100])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 13.167079913226685\n",
      "MSE: 399.0933987393499\n",
      "RMSE: 19.97732211131787\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(\"MAE: {}\".format(mae))\n",
    "print(\"MSE: {}\".format(mse))\n",
    "print(\"RMSE: {}\".format(np.sqrt(mse)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross-Validation and Hyper-parameters | Support Vector Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR, LinearSVR, NuSVR\n",
    "svr_pipe = Pipeline([\n",
    "    ('standarscaler', StandardScaler()),\n",
    "    ('regressor', SVR())\n",
    "])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cs = [0.001, 0.01, 0.1, 1, 10]\n",
    "gammas = [0.001, 0.01, 0.1, 1]\n",
    "kernels = ['linear', 'rbf', 'sigmoid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 60 candidates, totalling 300 fits\n",
      "[CV] regressor__C=0.001, regressor__gamma=0.001, regressor__kernel=linear \n",
      "[CV]  regressor__C=0.001, regressor__gamma=0.001, regressor__kernel=linear, score=0.5221868310469786, total= 1.6min\n",
      "[CV] regressor__C=0.001, regressor__gamma=0.001, regressor__kernel=linear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  1.9min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  regressor__C=0.001, regressor__gamma=0.001, regressor__kernel=linear, score=0.5603189593930469, total=  29.1s\n",
      "[CV] regressor__C=0.001, regressor__gamma=0.001, regressor__kernel=linear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:  2.7min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  regressor__C=0.001, regressor__gamma=0.001, regressor__kernel=linear, score=0.5752481382471257, total=  26.7s\n",
      "[CV] regressor__C=0.001, regressor__gamma=0.001, regressor__kernel=linear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:  3.5min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  regressor__C=0.001, regressor__gamma=0.001, regressor__kernel=linear, score=0.5388081220039732, total=  27.0s\n",
      "[CV] regressor__C=0.001, regressor__gamma=0.001, regressor__kernel=linear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:  4.3min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  regressor__C=0.001, regressor__gamma=0.001, regressor__kernel=linear, score=0.5315933240996253, total=  29.1s\n",
      "[CV] regressor__C=0.001, regressor__gamma=0.001, regressor__kernel=rbf \n",
      "[CV]  regressor__C=0.001, regressor__gamma=0.001, regressor__kernel=rbf, score=-0.027056272641825085, total=  34.5s\n",
      "[CV] regressor__C=0.001, regressor__gamma=0.001, regressor__kernel=rbf \n",
      "[CV]  regressor__C=0.001, regressor__gamma=0.001, regressor__kernel=rbf, score=-0.03026266031967384, total=  37.5s\n",
      "[CV] regressor__C=0.001, regressor__gamma=0.001, regressor__kernel=rbf \n"
     ]
    }
   ],
   "source": [
    "hyperparameters = {'regressor__C': Cs,\n",
    "                   'regressor__gamma': gammas,\n",
    "                   'regressor__kernel': kernels\n",
    "                  }\n",
    "\n",
    "rgs_svr = GridSearchCV(svr_pipe, hyperparameters, cv=5, verbose=5)\n",
    "rgs_svr.fit(X_train, y_train.ravel())\n",
    "rgs_svr.score(X_test, y_test.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15390,)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.ravel().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}